Some gut-based judgements on the effectiveness of data.
Great post, Benn. Your first para about the VC made me chuckle. I'd add a couple of observations:
- IMHO, a (very) important component of being data-driven is using data to define goals/outcomes, and then using those outcomes to frame decisions. There's so much pointless retrospective analysis done (especially in areas like marketing) that doesn't lead to any different decision being made because the team doesn't even know what they're aiming for. So if MTRX came to the pitch with a really crisp/interesting set of metrics that they were going to manage the business to, and a good story about why those metrics were important and spoke to how they saw themselves creating business differentiation, I'd be interested.
- Another benefit of the "counting cards" approach to using data is that over time, teams & leaders develop an intuitive grasp of the core dynamics of the business they're managing through the data (which, as an aside, is why stable, well-engineered analytical models are so important). Over time this intuitive understanding helps people to make better decisions, whether or not they actually use data to make those decisions, because they understand how the different measures of business performance are connected - effectively learning the business through the data it generates.
Yes. Its experience. One can think of the best 'data driven' efforts as the senses of the organizational entity. Experience takes time.
"Data is a means to an end, not an end in itself"- said no amatuer data professional ever!
Super weird to put Long View last, when they're described as having experience and intuition, and your criteria to pick is "talent and intuition". You seem to have an implicit bias that people who know an industry, regardless of what experience they have, don't have talent or intuition, and, furthermore, will be slow and indecisive?
Very crisp writing, I didn't even skim.
To add something on playing different games at different tables - if MTRX does find a competitive AI driven advantage, unassailable by the others, then they would win by 10x, but if not then they certainly fail.
Square Corner will win in most scenarios, but only with a marginal % lead.
To the casino analogy (I know very little about casinos), MTRX is playing a few high stakes poker hands, while Square Corner is grinding away counting cards.
I really enjoyed this piece — you nailed it. One data driven decision doesn't directly lead to an advantage or huge return, but rather a bunch of small ones added up over time.
Agree with your points (clarified after reading the footnotes): [paraphrasing] companies hit home runs by swinging more often - not by few sings of a better bat. But the better bat (data) allows them to swing more often. Companies with strong a priori statements on strategy or positioning (e.g. “our clothes will be this or that”) are single swings. Some will, by random luck alone, hit home runs. imho, the rate of such home runs will be *less* than a random draw (ie. Less than 1/n where n is the number of such companies). That is, intuition not only doesn’t help , but actually hurts. Yet, the statement does provide a nice hindsight narrative.
Data (and algorithms and insights) allows you to reflect on your swing, adjust and take another swing. That subsequent swings are more informed. You are informed by high-frequency-events (transactions, customer interactions, etc) which gives you many, many, many opportunities to adjust the swing.
Having provided analysis of fashion (mainly footwear ) businesses for over 40 years here's my 2c.
For a business to be successful the owners need to understand the business/industry really well and that's where they spend their time.
Companies and people like me can then use KPIs and processes developed over the decades to make the business work well operationally, and then apply analysis from the formulas derived over years of research to show how and where the business can grow.
To me, these are critical. Industry knowledge, well run operations, proper analysis.
eg one of the leading causes of retail failure is accumulation of unsaleable stock. It's incredibly misleading because assets accumulate when they are not assets and retailers struggle to convince themselves to write them off.
If you run B&M as well then my measure - stock vitality - is critical to using online to help clear stock from where it won't sell and therefore allow stocking a location with stock that might sell.
If you run an online only enterprise and I ask how are you going to add another $1m to turnover it's tricky. More advertising, Google adwords, etc all of which can cost more than the increase in notional profit.
Same question and you have B&M? Open another store.
Until ML and AI insisted they could do a better job and sort of won the marketing war we were adding significant profits to our clients. We still do, but those who believed the data approach have never been able to quite get there. In retail you see so many fads over the years.
So the company I would back is the one where the owners know their product space and customers really well, look to business operations as critical support, and are prepared to bring in outside help to achieve their goals.
Several companies have dragged me out of semi retirement for exactly this reason.
Loved this article, Ben.
I followed you the whole way through on this one, but got stuck at the positioning of Long View vs. Square Corner. I had my rankings flipped from yours (I had Long View at #3 and Square Corner at #5). From my understanding, your opposing ranking is suggesting that military precision in communication will outperform experience in the industry.
I saw the article you linked on "speed" and the importance of speed in analytical decision making. However, I'd be curious to hear any more thoughts you have on ranking Speed vs. Experience? I've always thought Speed is important, but have weighted Experience more heavily.
Side note - laughed at "a budding oak sprig that symbolizes your commitment to helping small saplings grow into enduring landmarks, and your “commitment” to sustainable investing"
This was a fun read!
Here's why I recommend subscribing to benn.substack: Usually offers unique insights but is always delightful to read!
So when data pundits talk about the need to “bring value” how do you interpret this when the value doesn’t exist at any point in time ? how do you measure the measurer?